In the book Artificial Intelligence for Dummies, I read the following:
Kernel machines are a family of algorithms, used for pattern analysis in [machine learning], that can operate on complex data by transforming it into a simpler form where patterns are more evident....The goal is to use similarity to determine the best solution to a problem. It's the kind of reasoning that determines that using a particular solution worked in a given circumstance at some previous time; therefore, using that solution for a similar set of circumstances should also work. One of the most recognizable outputs...is recommender systems. For example, when you buy a product on Amazon, the recommendation system comes up with other related products you might also want to buy.
Remember that old saying, "If you always do what you've always done, you're always going to get what you've always gotten"? Kernel machines aren't going to help anyone innovate!
My recent experiences on Amazon are that changing up the search terms to try to see other related items is much less effective than it used to be. It seems like the Amazon computer algorithms are pushing me toward looking and buying from a specific group of items instead of helping me broaden my options.
If the same kind of algorithms are being used in other areas, that's going to limit people's exposure to new ideas and decrease creative experiments and the sharing of the experiments' results in fields of science, nutrition, finance, entertainment, etc. What a boring, minimally-productive world it will become if we're all stuck in information silos.
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